Periodic Pattern Search on Time - Related

For many applications such as accounting, banking, business transaction processing systems, geographical information systems, medical record book keeping, etc., the changes made on their databases over time are a valuable source of information which can direct the future operation of the enterprise. In this thesis, we will focus on relational databases with historical data or, in other words, time-related data, and try to extract from them some useful knowledge about their periodic behavior. The discovered knowledge could provide user some future guidance, to which end techniques in knowledge discovery and data warehousing become important. Knowledge discovery and data warehousing have been increasingly important in handling and analyzing large databases e ciently and e ectively. We can take advantage of existing on-line analytical processing techniques widely used in knowledge discovery and data warehousing, and apply them on time-related data to solve periodic pattern search problems. The problems discussed in this presentation include two types. One is to nd periodic patterns of a time series with a given period, while the other is to nd a pattern with arbitrary length of period. The algorithms will be presented, along with their experimental results. iii Acknowledgments I would like to thank my senior supervisor Dr. Jiawei Han for his invaluable guidance, enthusiasm and nancial support throughout the course of this work. I am also very grateful to my supervisor Dr. Qiang Yang for his helpful comments and insightful suggestions during the research and writing of this thesis. I would also like to thank Dr. Veronica Dahl for taking the time to be my external examiner. Many other people have helped and contributed their time to the research of this thesis. My thanks to Ye Lu, Shan Cheng, and Bin Xia for their invaluable comments and suggestions. I would also like to take this opportunity to express my gratitude toward everyone in the Intelligent Database Laboratory of the School of Computing Science at Simon Fraser University, especially Sonny Chee, Qing Chen, Shan Cheng, Jenny Chiang, Micheline Kamber, Kris Koperski, Nebojsa Stefanovic, Bin Xia, Osmar Zaiane, Shuhua Zhang, Hua Zhu, for their valuable suggestions and help in these two years of study as well as their friendship. Thanks to all the other friends I have made at Simon Fraser University for making my stay at SFU an enjoyable period of time. Last but certainly not least, I will always be indebted to my family, especially my parents Feili Gong and Wei Wang, my grandparents Yu Gong, Dejing Wang, Shiren Wang, and Dezhi Li, and my sister Quan Gong. I would like to thank them for their support and con dence in me. My gratitude goes to everyone at home and my two aunts who generously supported my study. This thesis would not have been possible without all their kindness and encouragement. iv Dedication To my parents and grandparents. v